Ultra-short-term wind power forecasting based on personalized robust federated learning with spatial collaboration

Abstract

An ultra-short-term wind power forecasting method based on personalized robust federated learning (PRFL) is proposed to exploit spatio-temporal correlation in a privacy-preserving way to facilitate the collaboration between wind farms. Firstly, standard federated learning (FL) with a bidirectional long short-term memory neural network (Bi-LSTM) as the underlying forecasting model is adopted as the primary framework. Secondly, to mitigate FL's vulnerability to anomalies, a Geometric median based federated aggregation scheme is utilized instead of the traditional FedAvg algorithm, enabling enhancement of the robustness against anomalous updates from individual wind farms. Finally, a personalized federated learning (PFL) strategy incorporating transfer learning is proposed to alleviate the challenge of limited model applicability due to data heterogeneity in conventional FL approaches. The case study involving 30 wind farms demonstrates the superior forecasting accuracy of PRFL compared to traditional FL-based methods. In particular, PRFL yields relative improvements ranging from 2.67 % to 7.49 % compared to local forecasting method without spatial correlation, while ensuring data privacy through the local storage of raw data at each collaborative wind farm. Furthermore, empirical examinations incorporating artificially induced anomalous scenarios affirm PRFL's outstanding resilience in handling anomalies during collaborative training, surpassing the capabilities offered by conventional approaches.